Organizational Learning Supported by Reference Architecture Models: Industry 4.0 Laboratory Study

The wave of the fourth industrial revolution (Industry 4.0) is bringing a new vision of the manufacturing industry. In manufacturing, one of the buzzwords of the moment is "Smart production". Smart production involves manufacturing equipment with many sensors that can generate and transmit large amounts of data. These data and information from manufacturing operations are however not shared in the organization. Therefore the organization is not using them to learn and improve their operations. To address this problem, the authors implemented in an Industry 4.0 laboratory an instance of an emerging technical standard specific for the manufacturing industry. Global manufacturing experts consider the Reference Architecture Model Industry 4.0 (RAMI4.0) as one of the corner stones for the implementation of Industry 4.0. The instantiation contributed to organizational learning in the laboratory by collecting and sharing up-to-date information concerning manufacturing equipment. This article discusses and generalizes the experience and outlines future research directions

Optical subsystems of metis (multi element telescope for imaging and spectroscopy) on board of the solar orbiter mission

The Lyman- spectral line, at 121.6 nm, is a wavelength of great interest for solar environment exploration and astrophysics. It is an important hydrogen emission line and can give information on the dynamics of heated regions of space like the solar photosphere and corona. Among others, the instrument METIS (Multi Element Telescope for Imaging and Spectroscopy) will be on board the mission Solar Orbiter, an ESA mission in collaboration with NASA, that from the year 2018 will undertake a trip towards the Sun to explore the dynamics of the solar dynamo and its connection with corona and heliosphere. METIS will take images of the corona in the visible range and at Lyman-, studying shape and evolution of the processes expanding from the Sun to the heliosphere. In the CNR-IFN UOS Padova laboratories I employed deposition and characterization facilities to study the characteristics of materials and devices to be used as optical elements at Lyman- wavelength. A morphological characterization was carried on with an atomic force microscope (AFM) and an optical characterization revealed the performances of materials and devices. Variations in performances were related to modications in the experimental conditions and the acquired knowledge was used to optimize the performances of the nal product. Annealing is an approach never fully explored before to increment the optical quality of magnesium uoride thin lms, and consequently the reectivity of VUV optical elements. I conducted a study of the phenomena involved in the process and applied the procedure to the realization of improved mirrors for this spectral region. This work will present all the experimental steps that led to the realization of the nal devices and describe the characteristics of this novel annealing approach

Automated Modeling with Abstraction for Enterprise Architecture (AMA4EA):Business Process Model Automation in an Industry 4.0 Laboratory

The transformation towards the Industry 4.0 paradigm requires companies to manage large amounts of data. This poses serious challenges with regard to how effectively to handle data and extract value from it. The state-of-the-art research of Enterprise Architecture (EA) provides limited knowledge on addressing this challenge. In this article, the Automated Modeling with Abstraction for Enterprise Architecture (AMA4EA) method is proposed and demonstrated. An abstraction hierarchy is introduced by AMA4EA to support companies to automatically abstract data from enterprise systems to concepts, then to automatically create an EA model. AMA4EA was demonstrated at an Industry 4.0 laboratory. The demonstration showed that AMA4EA could abstract detailed data from the Enterprise Resource Planning (ERP) system and Manufacturing Execution System (MES) to be relevant for a business process model that provided a useful and simplified visualization of production process data. The model communicated the detailed business data in an easily understandable way to stakeholders. AMA4EA is an innovative and novel method that contributes new knowledge to EA research. The demonstration provides sufficient evidence that AMA4EA is useful and applicable in the Industry 4.0 environment

Initial performance of 'Rebo' wine grapes on different rootstocks and at various planting densities

O objetivo deste trabalho foi avaliar a influência de diferentes porta‑enxertos e densidades de plantio sobre o desempenho inicial da produtividade e da qualidade de uvas da cultivar Rebo, cultivada em região de alta altitude do estado de Santa Catarina, Brasil. Realizou-se um delineamento experimental de blocos ao acaso, em arranjo fatorial 5×3. Os tratamentos consistiram da combinação de cinco porta-enxertos ('101-14 Mgt', 'IAC 572', 'Paulsen 1103', 'Harmony' e 'VR 043‑43') com a uva Rebo e de três espaçamentos entre plantas (1,0, 1,2 e 1,5 m). As avaliações ocorreram durante as safras 2018/2019 e 2019/2020. Avaliaram-se as variáveis produtivas iniciais e as características físico-químicas das uvas. O porta-enxerto '101‑14 Mgt' apresentou a brotação mais precoce, e os porta-enxertos 'VR043-43' e 'IAC 572', as mais tardias. Os porta‑enxertos '101‑14 Mgt' e 'VR043-43' proporcionaram maior produtividade. Os espaçamentos de 1,0 e 1,2 m entre plantas aumentaram a produtividade inicial da vinha. Os porta-enxertos '101‑14 Mgt' e 'Harmony' contribuíram para diminuir a acidez total do mosto. O desempenho inicial da produtividade e da qualidade da uva vinífera 'Rebo' é favorecido pelo porta-enxerto '101-14 Mgt' às densidades de plantio de 1,0 a 1,2 m.The objective of this work was to evaluate the influence of different rootstocks and planting densities on the initial performance of the yield and quality of the Rebo grapes cultivars grown in a high altitude region of the state of Santa Catarina, Brazil. An experimental design with randomized complete blocks was carried out in a 5×3 factorial arrangement. The treatments consisted of the combination of five rootstocks ('101-14 Mgt', 'IAC 572', 'Paulsen 1103', 'Harmony', and 'VR 043-43') with the 'Rebo' grape, and of three spacing between plants (1.0, 1.2, and 1.5 m). The evaluations took place during the 2018/2019 and 2019/2020 crop seasons. The initial yield variables and the physicochemical characteristics of the grapes were evaluated. The '101-14 Mgt' rootstock provided early shoots, and the 'VR043‑43' and 'IAC 572' rootstocks the latest shoots. The '101-14 Mgt' and 'VR043-43' rootstocks provided a higher yield. Plant spacing at 1.0 and 1.2 m increased the initial yield of the vineyard. The '101-14 Mgt' and 'Harmony' rootstocks contributed to the lower total acidity of must. The initial performance of the yield and quality of 'Rebo' wine grape is favored by the '101-14 Mgt' rootstock at the planting densities of 1.0 to 1.2 m

AMPA receptor GluA2 subunit defects are a cause of neurodevelopmental disorders.

AMPA receptors (AMPARs) are tetrameric ligand-gated channels made up of combinations of GluA1-4 subunits encoded by GRIA1-4 genes. GluA2 has an especially important role because, following post-transcriptional editing at the Q607 site, it renders heteromultimeric AMPARs Ca2+-impermeable, with a linear relationship between current and trans-membrane voltage. Here, we report heterozygous de novo GRIA2 mutations in 28 unrelated patients with intellectual disability (ID) and neurodevelopmental abnormalities including autism spectrum disorder (ASD), Rett syndrome-like features, and seizures or developmental epileptic encephalopathy (DEE). In functional expression studies, mutations lead to a decrease in agonist-evoked current mediated by mutant subunits compared to wild-type channels. When GluA2 subunits are co-expressed with GluA1, most GRIA2 mutations cause a decreased current amplitude and some also affect voltage rectification. Our results show that de-novo variants in GRIA2 can cause neurodevelopmental disorders, complementing evidence that other genetic causes of ID, ASD and DEE also disrupt glutamatergic synaptic transmission